Leadership Wants AI in the Contact Center by 2027: Where to Start and How to Know It’s Working

Customer experience and operations leaders are developing plans to bring artificial intelligence (AI) into the contact center by 2027. The pressure is real: in a Gartner survey of 321 customer service leaders, 91% reported pressure from executive leadership to implement

That expectation is understandable. AI promises faster customer service, more efficient operations, better agent support, and improved experience across channels, but knowing AI can deliver these benefits and knowing where to introduce it are different.

For many organizations, the biggest challenge is deciding which operational problem deserves attention first, preparing the team for change, and establishing a reliable way to measure whether the investment is paying off.

A successful contact center AI strategy begins with a clear understanding of what’s holding the operation back.

Start With the Problem, Not the Technology

Before evaluating a virtual agent, agent assist platform, or automated quality monitoring solution, take a closer look at the contact center’s current performance. What is costing the operation the most time, money, or customer goodwill?

For one organization, the biggest issue might be lengthy agent onboarding, with new employees taking too long to become comfortable with systems, policies, and customer conversations. For another, long wait times and high contact volumes may be pressuring service levels. Other operations might struggle with unnecessary transfers, inconsistent responses, or declining customer satisfaction scores.

These challenges require different solutions. Introducing a virtual agent won’t necessarily improve agent onboarding, just as deploying agent assist won’t automatically resolve every issue contributing to long customer wait times.

Start by identifying one problem that has a meaningful operational impact. Review performance reports, listen to customer interactions, speak with frontline employees, and examine where delays or inconsistencies occur.

Then ask whether AI can realistically address the issue. AI won’t automate everything, but a clearly defined problem makes it easier to see where it will have a measurable impact.

Build an AI Roadmap Around the Operation

Most contact center challenges fall into one of four stages: preparing agents to perform, supporting them during live customer interactions, measuring service quality, and understanding why customers are contacting the business. Each stage calls for a different type of AI tool. AI-guided training and knowledge tools can shorten onboarding, agent assist can provide real-time guidance during live calls, automated QA can review far more interactions than manual sampling, and interaction analytics can reveal the drivers behind contact volume.

These categories provide a useful starting point, but the right priority depends on the operation’s needs, existing systems, and readiness for change. In regulated industries such as healthcare and financial services, review data privacy and compliance requirements before any AI tool handles customer information.

Once the priority is clear, create a practical AI roadmap. Rather than attempting several major implementations at once, select one use case, establish the necessary processes, and give the organization time to learn from the results.

A focused rollout makes it easier to identify what works, address problems early, and build confidence before expanding AI adoption.

Three Common AI Rollout Mistakes to Avoid

Even a promising AI solution can disappoint when implementation overlooks the people and processes that make a contact center work.

Three mistakes are particularly worth avoiding.

1. Launching a Virtual Agent with Outdated Information

A virtual agent is only as useful as the information behind its responses.

If it relies on outdated policies, incomplete knowledge articles, or inconsistent product information, customers may receive inaccurate or confusing answers. Instead of reducing contact volume, the technology can create additional interactions when customers need to contact a human representative to correct the problem.

Before launch, review the knowledge sources the AI will use. Assign clear ownership for maintaining that information, establish an update process, and test responses against realistic customer questions. Also define when the virtual agent should direct a customer to a human employee, and make it clear to customers when they are interacting with AI. Knowledge quality is becoming a priority across the industry: Gartner found that 58% of service leaders plan to upskill agents into knowledge management specialists.

2. Introducing New Tools Without Preparing Agents

Technology adoption doesn’t happen simply because a new platform becomes available.

Agents need to understand what the tool does, when to use it, and how it fits into their existing responsibilities. Without practical training, employees may ignore recommendations, struggle with unfamiliar workflows, or develop workarounds that limit the technology’s value.

This is especially important with agent assist tools. Employees should understand how AI-generated suggestions are produced, how to verify information, and when their own judgment should take priority.

Involve agents early, provide hands-on training, and collect feedback during the rollout. Their experience can reveal workflow issues that may not appear during testing.

3. Skipping the Performance Baseline

One of the easiest ways to misjudge an AI investment is to introduce it without documenting current performance.

If leadership doesn’t know how long customers currently wait, how frequently contacts are transferred, or how agents perform before implementation, it becomes difficult to determine whether the new technology has improved anything.

Before launching, record the relevant performance metrics. Use consistent measurement methods and compare post-launch results against that original baseline.

Without this step, teams risk confusing increased activity with improvement.

What Should Leaders Measure During the First 90 Days?

Treat the first 90 days after implementation as a learning period. The objective is to understand whether the solution is improving the operation, whether employees are adopting it, and where adjustments are needed.

A monthly review gives leadership a practical way to evaluate progress without getting distracted by every fluctuation in individual performance.

Month 1: Establish Adoption and Early Performance

The first month is about confirming that the technology is being used as intended.

Look at:

• Agent adoption: Are employees actively using the tool? How frequently, and in which workflows?
• System reliability: Is the technology available and functioning consistently?
• Early efficiency: Are there initial improvements in response times, information retrieval, or task completion?
• Employee feedback: Are agents finding the tool helpful, or is it adding friction to their work?

Low adoption may indicate that training, communication, or workflow integration needs attention.

Month 2: Evaluate Operational Improvements

By the second month, leaders should have more information about whether the solution is affecting day-to-day performance.

Compare results against the pre-launch baseline and examine:

• Faster answers: Are customers receiving information or assistance more quickly?
• Fewer transfers and escalations: Is the transfer rate declining? For virtual agents, what share of contacts is contained without escalation, and are escalated customers reaching the right resource?
• First contact resolution (FCR): Are more interactions being resolved on the first contact, without repeat calls?
• Customer satisfaction: Are CSAT scores and customer feedback showing improvement?

Look beyond individual metrics. For example, shorter average handling times aren’t necessarily positive if repeat contacts increase because customers aren’t getting complete answers.

The most useful success metrics reflect the overall customer and employee experience in addition to operational speed.

Month 3: Decide What to Improve, Expand, or Reconsider

By the third month, leadership should have enough evidence to make a more informed decision about the next phase.

Review whether the original business problem has improved. Check whether agent adoption is increasing, customers are experiencing better service, and the operation is seeing sustainable efficiency gains.

If results are positive, consider expanding the use case or introducing another AI capability. If performance is inconsistent, investigate the underlying cause before increasing investment.

Perhaps the knowledge base needs attention, employees need additional training, or the solution isn’t aligned with the actual workflow.

The important thing is to make decisions based on evidence rather than assumptions.

Make AI Accountable to Better Customer Experiences

Bringing AI into the contact center by 2027 should be more than a technology milestone. It’s an operational improvement initiative with clear objectives, accountable ownership, and measurable outcomes.

The strongest contact center AI strategies begin with a business problem, introduce technology where it can make a meaningful difference, and give employees the support they need to use it effectively. Many contact center partners, including The Office Gurus, apply this approach by pairing automated QA with real-time agent guidance, so quality insights reach agents during the conversation rather than after it.

They also recognize that successful implementation doesn’t end at launch. Regular measurement, employee feedback, and continuous improvement are essential to making AI work in production.

For leaders deciding where to begin, the most useful first step is understanding which part of the operation needs the most help.

See which stage to start with: Which Contact Center AI Solution to Deploy First.